Method for voice identification and device using same

ABSTRACT

An electronic device may include: a memory; a sound sensor; and a processor, wherein the processor is configured to: receive, from the sound sensor, sound data including a first piece of data corresponding to a first frequency band and a second piece of data corresponding to a second frequency band different from the first frequency band; receive voice data related to a voice of a registered user from the memory; perform voice identification by comparing the first piece of data and the second piece of data with the voice data related to the voice of the registered user; and determine an output based on a result of the voice identification.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on and claims priority under 35 U.S.C. § 119to Korean Patent Application No. 10-2021-0025962, filed on Feb. 25,2021, in the Korean Intellectual Property Office, the disclosure ofwhich is incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to a method for voice identification using amulti-frequency band sound sensor and a device for voice identification.

2. Description of the Related Art

Voice identification technology has been used in various fields. Voiceidentification may provide convenience by identifying a user's voiceinput to an electronic device. For example, voice identification may beperformed on a user's voice input to an electronic device to completeonline shopping.

An electronic device including a sound sensor or the like may performvoice identification using an artificial intelligence (AI) assistant(for example, an AI voice agent). For example, services or systems suchas voice shopping, ordering through a kiosk of a restaurant, reservationticketing systems, financial services, and contactless response servicesmay be set to be available for only users of particular electronicdevices who have been previously identified by voice identification.However, if users are able to be identified using only their voices,users of electronic devices may more conveniently use services orsystems in various fields.

Voice identification methods are required to accurately identify thevoice of authorized users. A voice user interface (VUI) may include avoice recognition technique, a voice synthesis technique, a speakerrecognition technique, and the like. A VUI may recognize the voice of alegitimate user of an electronic device through voice identification.

In addition to the use of voice identification in various fields, therehas been increasing demand for techniques for improving the accuracy andsecurity of voice identification. The accuracy of voice identificationmay decrease due to variations in the voice of a user of an electronicdevice. For example, the accuracy of voice identification may bedecreased due to: time-varying characteristics of organs such as thevocal cords, neck, oral cavity, and nasal cavity of a user of anelectronic device; and short-term body condition variations caused bydiseases such as a cold.

Training data reflecting various surrounding environments and user'sconditions may be required for accurate voice identification using aVUI. However, it is difficult to prepare a training database reflectingall surrounding environments and user's conditions.

SUMMARY

One or more example embodiments provide a method for voiceidentification using a multi-frequency band sound sensor and a devicefor voice identification.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments of the disclosure.

According to an aspect of the disclosure, there is provided anelectronic device comprising: a memory; a sound sensor; and a processorconfigured to: receive, from the sound sensor, sound data comprising afirst piece of data corresponding to a first frequency band and a secondpiece of data corresponding to a second frequency band different fromthe first frequency band; receive voice data related to a voice of aregistered user from the memory; perform voice identification bycomparing the first piece of data and the second piece of data with thevoice data related to the voice of the registered user; and determine anoutput based on a result of the voice identification.

The sound sensor may comprise a plurality of mechanical oscillatorsconfigured to sense the sound data according to frequency bands.

The plurality of mechanical oscillators may comprise: at least one firstmechanical oscillator configured to sense the sound data in a firstband; and at least one second mechanical oscillator configured to sensethe sound data in a second band.

The voice data related to the voice of the registered user may comprisefirst voice data in the first frequency band and second voice data inthe second frequency band.

The processor may be further configured to perform the voiceidentification by comparing the first piece of data with the first voicedata in the first frequency band, and the second piece of data with thesecond voice data in the second frequency band.

The processor may be further configured to perform the voiceidentification by: determining a first representative valuecorresponding to the first piece of data and a second representativevalue corresponding to the second piece of data; and comparing aweighted sum of the first representative value and the secondrepresentative value with a threshold value.

The processor may be further configured to perform the voiceidentification by: determining a weighted sum of raw data of the firstpiece of data and raw data of the second piece of data; and comparingthe weighted sum with a threshold value.

The processor may be further configured to: determine, based on a resultof the comparing the first piece of data and the first voice data in thefirst frequency band, whether the sound data matches the voice of theregistered user in the first frequency band; and determine, based on aresult of the comparing the second piece of data and the second voicedata in the second frequency band, whether the sound data matches thevoice of the registered user in the second frequency band.

The processor may be further configured to determine that the voiceidentification is successful when the weighted sum is greater than thethreshold value.

The processor may be further configured to determine that the voiceidentification is successful when a sum of a result of the determinationin the first frequency band and a result of the determination in thesecond frequency band is greater than a threshold value.

According to another aspect of the disclosure, there is provided amethod of identifying a voice using an electronic device, the methodcomprising: receiving, from a sound sensor, sound data comprising afirst piece of data corresponding to a first frequency band and a secondpiece of data corresponding to a second frequency band different fromthe first frequency band; receiving voice data related to a voice of aregistered user from a memory; performing voice identification bycomparing the first piece of data and the second piece of data with thevoice data related to the voice of the registered user; and determiningan output based on a result of the voice identification.

The sound sensor may comprise a plurality of mechanical oscillators, andthe method further comprises sensing the sound data according tofrequency bands by using the plurality of mechanical oscillators.

The sensing of the sound data according to the frequency bands maycomprise: sensing the sound data in a first band by using at least onefirst mechanical oscillator among the plurality of mechanicaloscillators; and sensing sound data in a second band by using at leastone second mechanical oscillator among the plurality of mechanicaloscillators.

The voice data related to the voice of the registered user may comprisefirst voice data in the first frequency band and second voice data inthe second frequency band.

The performing of the voice identification may comprise: comparing thefirst piece of data with the first voice data in the first frequencyband; and comparing the second piece of data with the second voice datain the second frequency band.

The performing of the voice identification may further comprise:determining a first representative value corresponding to the firstpiece of data and a second representative value corresponding to thesecond piece of data; and comparing a weighted sum of the firstrepresentative value and the second representative value with athreshold value.

The performing of the voice identification may further comprise:determining a weighted sum of raw data of the first piece of data andraw data of the second piece of data; and comparing the weighted sumwith a threshold value.

The method may further comprise: determining, based on result of thecomparing the first piece of data and the first voice data in the firstfrequency band, whether the sound data matches the voice of theregistered user in the first frequency band; and determining, based on aresult of the comparing the second piece of data and the second voicedata in the second frequency band, whether the sound data matches thevoice of the registered user in the second frequency band.

The determining of the output may comprise determining that the voiceidentification is successful when the weighted sum is greater than thethreshold value.

The determining of the output may comprise determining that the voiceidentification is successful when a sum of a result of the determiningin the first frequency band and a result of the determining in thesecond frequency band is greater than a threshold value.

According to another aspect of the disclosure, there is provided anelectronic device comprising: a memory storing one or more instructions;a processor configured to execute the one or more instructions to:receive sound data comprising a first sound data corresponding to afirst frequency band and a second sound data corresponding to a secondfrequency band different from the first frequency band; receive storedvoice data related to a voice of a user, the stored voice datacomprising a first voice data corresponding to the first frequency bandand a second voice data corresponding to the second frequency band; andperform voice identification by separately comparing the first sounddata with the first voice data and the second sound data with the secondvoice data.

According to another aspect of the disclosure, there is provided anelectronic device comprising: a memory storing one or more instructions;a processor configured to execute the one or more instructions to:receive sound data comprising a first sound data corresponding to afirst characteristic feature and a second sound data corresponding to asecond characteristic feature; receive stored voice data related to avoice of a user, the stored voice data comprising a first voice datacorresponding to the first characteristic feature and a second voicedata corresponding to the second characteristic feature; and performvoice identification by separately comparing the first sound data withthe first voice data and the second sound data with the second voicedata.

According to another aspect of the disclosure, there is provided amethod comprising: receiving sound data comprising a first sound datacorresponding to a first characteristic feature and a second sound datacorresponding to a second characteristic feature; receiving stored voicedata related to a voice of a user, the stored voice data comprising afirst voice data corresponding to the first characteristic feature and asecond voice data corresponding to the second characteristic feature;and performing voice identification by separately comparing the firstsound data with the first voice data and the second sound data with thesecond voice data.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a block diagram illustrating elements of an electronic deviceaccording to various example embodiments;

FIG. 2A is an example view illustrating a method of identifying voicescollected using a microphone according to various example embodiments;

FIG. 2B is an example view illustrating a method of identifying voicescollected using a sound sensor according to various example embodiments;

FIG. 3 is an example view illustrating a multi-band sound sensoraccording to various example embodiments;

FIG. 4 is an example view illustrating the energy of voice data of usersfor comparison according to frequency bands by a voice identificationmethod according to various example embodiments;

FIG. 5 is an example view illustrating a voice identification method fordetermining whether there is an error in voice data of a user accordingto various example embodiments;

FIG. 6 is a flowchart illustrating a voice identification methodaccording to various example embodiments;

FIG. 7 is a flowchart illustrating a method of identifying a voice ineach frequency band according to various example embodiments;

FIG. 8 is a flowchart illustrating a process of deriving results in avoice identification method according to various example embodiments;

FIGS. 9A to 9C are example views illustrating voice identificationmethods according to various example embodiments; and

FIGS. 10A and 10B are example views illustrating voice identificationmethods using neural networks according to various example embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to example embodiments, examples ofwhich are illustrated in the accompanying drawings, wherein likereference numerals refer to like elements throughout. In this regard,the example embodiments may have different forms and should not beconstrued as being limited to the descriptions set forth herein.Accordingly, the example embodiments are merely described below, byreferring to the figures, to explain aspects. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. Expressions such as “at least one of,” whenpreceding a list of elements, modify the entire list of elements and donot modify the individual elements of the list.

The terms used in example embodiments are selected based on generalterms currently widely used in the art, but the terms may vary accordingto the intention of those of ordinary skill in the art, precedents, ornew technology in the art. Also, some terms may be arbitrarily selectedby the applicant(s), and in this case, the meaning of the selected termsare described in the detailed description of the disclosure. Thus, theterms used herein should not be construed based on only the names of theterms but should be construed based on the meaning of the terms togetherwith the description throughout the present disclosure.

In the following descriptions of the example embodiments, expressions orterms such as “constituted by,” “formed by,” “include,” “comprise,”“including,” and “comprising” should not be construed as alwaysincluding all specified elements, processes, or operations, but may beconstrued as not including some of the specified elements, processes, oroperations, or further including other elements, processes, oroperations.

In the example embodiments, “units” and/or “modules” of the disclosuremay be implemented by a hardware component, a software component or acombination including both hardware and software components. Forexample, “units” and/or “modules” of the disclosure may be implementedby circuitry.

In the example embodiments, when a portion or element is referred to asbeing connected to another portion or element, the portion or elementmay be directly connected to the other portion or element, or may beelectrically connected to the other portion or element with interveningportions or elements being therebetween.

In addition, although terms such as “first” and “second” are used todescribe various elements, these elements should not be limited by theseterms. These terms are only used to distinguish one element from otherelements.

Embodiments will now be described with reference to the accompanyingdrawings. However, the idea of the present disclosure may be implementedin various ways and is not limited to the example embodiments describedherein.

FIG. 1 is a block diagram illustrating elements of an electronic device100 according to various example embodiments.

Referring to FIG. 1, the electronic device 100 may include a processor110, a sound sensor 120, and a memory 130. For example, the electronicdevice 100 may receive voice data of a user. The electronic device 100may include a portable terminal and an electronic unit equipped with anunmanned response system (for example, a kiosk). Elements of theelectronic device 100 which are shown in FIG. 1 are merely non-limitingexamples, and the electronic device 100 may include other elementsinstead of or in addition to the elements shown in FIG. 1.

Referring to FIG. 1, the sound sensor 120 may sense sound informationthrough a sensor module and the processor 110 may receive the soundinformation. For example, the processor 110 may receive, in eachfrequency band, at least one piece of sound data which is sensed usingthe sound sensor 120. The processor 110 may separately receive a firstpiece of data including sound data in a first band and a second piece ofdata including sound data in a second band.

Referring to FIG. 1, the processor 110 may perform voice identificationfor one or more users who are registered as users of the electronicdevice 100. For example, the processor 110 may perform voiceidentification for User 1, User 2, and User 3 of the electronic device100. The electronic device 100 may store data related to the voice ofUser 1 (voice data of user1), data related to the voice of User 2, anddata related to the voice of User 3 in the memory 130. The processor 110may compare, in each band, received sound data with data related to thevoice of at least one user who is preset as a user of the electronicdevice 100.

Referring to FIG. 1, the processor 110 may calculate, in each band, atleast one piece of sound data sensed using the sound sensor 120. Forexample, the processor 110 may calculate a first representative valuecorresponding to the first piece of data, and a second representativevalue corresponding to the second piece of data. The processor 110 maycompare a weighted sum of the first representative value and the secondrepresentative value with a threshold value. According to an exampleembodiment, the threshold value may be preset. According to anotherexample embodiment, the processor 110 may calculate a weighted sum ofraw data of the first piece of data and raw data of the second piece ofdata. The processor 110 may compare the calculated weighted sum with athreshold value. According to an example embodiment, the threshold valuemay be preset. While performing voice identification, the processor 110may determine that the voice identification is successful when acalculated result is greater than a preset threshold value. Whileperforming voice identification, the processor 110 may determine thatthe voice identification is unsuccessful when a calculated result isless than a preset threshold value. According to an example embodiment,when more than fifty percent of results out of the determination in thefirst band, the determination in the second band, . . . , and thedetermination in an nth band (for example, n is an odd number) areacceptances (for example, when 1, among 1 and 0, refers to acceptance,and 0 refers to rejections, and the number of 1s is greater than n/2),the processor 110 may determine that the voice identification issuccessful. When more than fifty percent of results out of thedetermination in the first band, the determination in the second band, .. . , and the determination in the nth band (for example, n is an oddnumber) are rejections (for example, when 1, among 1 and 0, refers toacceptance and 0 refers to rejections, and the number of 0s is greaterthan n/2), the processor 110 may determine that the voice identificationis unsuccessful. However, the method of determining voice identificationis not limited to determining voice identification as successful basedon the majority of results of determination in a plurality of bands, andfor example, voice identification may be determined as successful basedon a preset number of determination results which is preset according todesign specifications of a manufacture. According to an exampleembodiment, the preset number may be 2.

Referring to FIG. 1, the sound sensor 120 may detect at least one pieceof sound data. For example, the sound sensor 120 may detect, as sounddata, a voice input in response to a starting phrase (for example, agreeting) of an artificial intelligence (AI) assistant built in andexecuted on the electronic device 100. The sound sensor 120 may includea plurality of mechanical oscillators, and the plurality of mechanicaloscillators may sense sound data according to frequency bands. Accordingto an example embodiment, the sound sensor 120 may include twentymechanical oscillators (i.e., first oscillator, second oscillator, . . .twentieth oscillator), and the mechanical oscillators may have differentdetectable frequency bands. However, the number of mechanicaloscillators are not limited to twenty according to another exampleembodiment. The sound sensor 120 may detect sound data in the first bandusing a mechanical oscillator (for example, the first mechanicaloscillator). The sound sensor 120 may detect sound data in the secondband using two or more mechanical oscillators (for example, the secondmechanical oscillator and the seventh mechanical oscillator). Thedetectable frequency band of one mechanical oscillator or two or moremechanical oscillators, and the combination of mechanical oscillatorsare not limited to the examples above, but may be customized accordingto user settings.

Referring to FIG. 1, data related to the voice of a user of theelectronic device 100 may be stored in the memory 130. For example, datarelated to the voice of User 1, User 2, and User 3, who are set as usersof the electronic device 100, may be stored in the memory 130. Thememory 130 may store data related to arbitrary constants which theprocessor 110 may use to calculate the first representative valuecorresponding to the first piece of data and the second representativevalue corresponding to the second piece of data. For example, the memory130 may store data related to a constant c1 required to calculate sounddata in the first band as a first representative value, and a constantc2 required to calculate sound data in the second band as a secondrepresentative value. The memory 130 may store data related to thresholdvalues with which the processor 110 may compare results of calculationduring voice identification. For example, the memory 130 may store datarelated to a threshold value to be compared with a weighted sum ofrepresentative values and a threshold value to be compared with aweighted sum of raw data. The memory 130 may store data in advance, ormay store data in real time through an external connection port of theelectronic device 100 or a communication module that may be included inthe electronic device 100. For example, the communication module may bea wired communication module or a wireless communication module.

Although the case in which the processor 110 of the electronic device100 performs voice identification for users has been described withreference to FIG. 1, the processor 110 may transmit at least one pieceof sound data sensed using the sound sensor 120 to a separate serverthrough a communication module (not shown) in other example embodiments.In this case, the server may include a separate memory and a separateprocessor, and data related to the voice of users of the electronicdevice 100 may be previously stored in the separate memory. Theprocessor of the server may perform user's voice identification on theat least one piece of sound data received through the communicationmodule. The processor of the server may perform voice identification inthe same manner as the processor 110 of the electronic device 100performs voice identification.

FIG. 2A is an example view illustrating a method of identifying a voicecollected using a microphone according to various example embodiments.

Referring to FIG. 2A, the microphone of an electronic device (forexample, the electronic device 100 shown in FIG. 1) may receive sounddata (for example, a voice of an authenticated user/unauthenticated userwhich is input to the electronic device). Voice identification may beperformed by a processor (for example, the processor 110 shown inFIG. 1) that may be included in the electronic device, a voiceidentification module (for example, a module capable of performing voiceidentification) including a processor, or the like. The voiceidentification module may perform voice identification by analyzing asignal (for example, a voice or sound data) acquired using themicrophone to compare the signal with data (for example, data related tothe voice of User 1, data related to the voice of User 2, or datarelated to the voice of User 3) related to the voice of a registereduser (for example, User 1, User 2, or User 3 of the electronic device)in each frequency band. The voice identification module may determinethe voice identification for identifying a registered user as successfulor unsuccessful based on results of the comparison (for example, referto the final decision in FIG. 2A).

FIG. 2B is an example view illustrating a method of identifying a voicecollected using a sound sensor according to various example embodiments.

Referring to FIG. 2B, the sound sensor (for example, the sound sensor120 shown in FIG. 1 or a multi-band sound sensor shown in FIG. 2B) mayinclude an array of a plurality of mechanical oscillators unlike themicrophone shown in FIG. 2A. Each of the mechanical oscillators may havea resonant frequency according to the shape thereof and may function asa band pass filter (in particular, an acoustic band pass filter) for agiven frequency band. For example, the mechanical oscillators may have aharmonic resonant mode and may thus have frequency band characteristics(variable transfer characteristics) in a wide frequency range unlikeanalog filters or digital filters.

Referring to FIG. 2B, the sound sensor of an electronic device (forexample, the electronic device 100 shown in FIG. 1) may detect sounddata (for example, a voice of a user which is input to the electronicdevice). According to an example embodiment the user may be anauthenticated user or an unauthenticated user. According to an exampleembodiment, voice identification may be performed by a processor (forexample, the processor 110 shown in FIG. 1) that may be included in theelectronic device. In another embodiment, voice identification may beperformed by a separate server that receives the sound data from theelectronic device by data communication. In this case, the voiceidentification may be performed by a processor of the server.

A voice identification module may perform voice identification byanalyzing a signal (for example, a voice or sound data) acquired usingthe sound sensor to determine the signal by comparing the signal withdata related to the voice of a registered user in each frequency band.According to an example embodiment, the voice identification module maycompare data related to the voice of User 1, data related to the voiceof User 2, or data related to the voice of User 3 with a registered User1, User 2, or User 3 of the electronic device in each frequency band.

When performing voice identification, the processor may perform a voiceidentification process in each sub-band and may determine final resultsof the voice identification by applying a final determination algorithmto decisions in the sub-bands. For example, the final determinationalgorithm may include a voting algorithm, a weighted sum algorithm, aneural network algorithm, or the like. The processor may use at leastone of the listed determination algorithms according to the voiceidentification process in each sub-band and results of decisions in thesub-bands. The voice identification module may determine the voiceidentification as successful or unsuccessful based on results obtainedusing the final determination algorithm (for example, refer to the finaldecision shown in FIG. 2B).

FIG. 3 is an example view illustrating a multi-band sound sensor as anexample of a sound sensor 120 according to various example embodiments.

Referring to FIG. 3, the sound sensor 120 of an electronic device (forexample, the electronic device 100 shown in FIG. 1) may detectmulti-band sound data. For example, the sound sensor 120 may detect atleast one piece of sound data. For example, the sound sensor 120 maydetect, as sound data, a voice input in response to a starting phrase(for example, a greeting) of an AI assistant built in and executed inthe electronic device.

Referring to FIG. 3, the sound sensor 120 may include a plurality ofmechanical oscillators. For example, in the sound sensor 120 shownaccording to an example embodiment in FIG. 3, one long vertical bar mayrefer to one mechanical oscillator. The sound sensor 120 may havedifferent detectable frequency bands according to the lengths of themechanical oscillators. For example, a high frequency band may bedetected with a short mechanical oscillator (or a thick mechanicaloscillator). In FIG. 3, one mechanical oscillator may sense sound datain a first band 310 and four mechanical oscillators may sense sound datain a second band 320. The second band 320, in which the four oscillatorshaving a great thickness sense sound data, may be higher than the firstband 310. Sound data in a third band 330 may be sensed using threemechanical oscillators, and because sound data in the third band 330 issensed using resonant frequency characteristics, the third band 330 maybe lower than the second band 320. The example described with referenceto FIG. 3 is merely a non-limiting example, and the sound sensor 120including the plurality of mechanical oscillators may be differentlyconfigured according to manufacturing process specifications.

Referring to FIG. 3, the sound sensor 120 may detect sound data in aplurality of frequency bands by using one or more mechanicaloscillators. For example, sound data input to the sound sensor 120 maybe sensed distinguishably in the first band 310, the second band 320,and the third band 330. In another example embodiment, sound data sensedusing the sound sensor 120 may be transmitted to a processor (forexample, the processor 110 shown in FIG. 1) distinguishably in the firstband 310, the second band 320, and the third band 330. The process ofdividing sound data according to frequency bands using the sound sensor120 or the processor may be performed by a method of sensing sound dataaccording to frequency bands previously set in the sound sensor 120, amethod in which sound data sensed using the sound sensor 120 is receivedby the processor distinguishably in preset frequency bands, or acombination of the two methods.

FIG. 4 is an example view illustrating the energy of voice data of usersfor comparison according to frequency bands by a voice identificationmethod according to various embodiments.

A processor (for example, the processor 110 shown in FIG. 1) may performvoice identification for a plurality of users registered as users of anelectronic device (for example, the electronic device 100 shown in FIG.1). For example, the processor may perform voice identification for User1, User 2, and User 3 registered as users of the electronic device. Theprocessor may determine whether received sound data matches at least oneof User 1, User 2, and User 3. For example, the processor may comparereceived sound data with voice data of User 1, voice data of User 2, andvoice data of User 3 which are stored in a memory (for example, thememory 130 shown in FIG. 1) to determine whether the received sound datamatches at least one of User 1, User 2, and User 3.

Referring to FIG. 4, registered users of the electronic device may beUser 1, User 2, and User 3. For example, voice data 420 may include data421 related to User 1's voice, data 422 related to User 2's voice, anddata 423 related to User 3's voice.

Referring to FIG. 4, the energy of data related to users' voicesreceived in an entire frequency band 410 is shown according to frequencybands. For example, the energy of the data 421 related to User 1's voicein the entire frequency band 410 may correspond to the intersectionbetween the “User 1's voice” row and the “entire frequency band 410”column in the table shown in FIG. 4. The energy of the data 422 relatedto User 2's voice in the entire frequency band 410 may correspond to theintersection between the “User 2's voice” row and the “entire frequencyband 410” column in the table shown in FIG. 4. The energy of the data423 related to User 3's voice in the entire frequency band 410 maycorrespond to the intersection between the “User 3's voice” row and the“entire frequency band 410” column in the table shown in FIG. 4.

Referring to FIG. 4, the energy of data related to users' voice infrequency bands 310, 320, and 330 may be stored in the memory of theelectronic device. For example, the data 420 related to User 1's voice,User 2's voice, and User 3's may be previously stored in the memory andmay be compared with sound data sensed in real time in the frequencybands.

The data 421 related to User 1's voice, the data 422 related to User 2'svoice, and the data 423 related to User 3's voice may be stored in thememory 130 of the electronic device 100. The processor 110 may compare,in each band, received sound data with voice data of at least one userwho is previously set as a user of the electronic device 100. Forexample, sound data, which is compared in each band with data 420related to a voice of at least one user, may refer to energy in a firstband 310, a second band 320, and a third band 330.

Referring to FIG. 4, the energy of sound data sensed by a sound sensor(for example, the sound sensor 120 shown in FIG. 1) may have adistribution in the first band 310, which corresponds to theintersection between the “first band 310” column and a “User n's voice”row (data 421, 422, or 423 related to User n's voice). For example,voice identification may be performed to identify whether a user whoinputs sound data into the electronic device is User 1. Beforeperforming voice identification for identifying User 1, the processormay store the data 421 related to User 1's voice in the memory. In thiscase, the processor may receive the data 421 related to User 1's voicein each frequency band through the sound sensor configured to sensesound data in each frequency band, and may store the data 421 related toUser 1's voice in the memory. The processor may perform voiceidentification by comparing the data 421 related to User 1's voice,which is received in each frequency band and stored in the memory, withsound data which is thereafter received in each frequency band. Forexample, the processor may compare the data 421 related to User 1'svoice, which is received in the first band 310, the second band 320, andthe third band 330 and stored in the memory, with sound data which isthereafter received in the first band 310, the second band 320, and thethird band 330. The processor may determine, using a voiceidentification algorithm based on results of the comparison, whether theuser who has input the current sound data is User 1 (for example,determining voice identification as successful or unsuccessful).

Referring to FIG. 4, the energy of sound data sensed by the sound sensormay have a distribution in the second band 320, which corresponds to theintersection between the “second band 320” column and a “User n's voice”row (data 421, 422, or 423 related to User n's voice). For example,voice identification may be performed to identify whether a user whoinputs sound data into the electronic device is User 2. Beforeperforming voice identification for identifying User 2, the processormay store the data 422 related to User 2's voice in the memory. In thiscase, the processor may receive the data 422 related to User 2's voicein each frequency band through the sound sensor configured to sensesound data in each frequency band, and may store the data 422 related toUser 2's voice in the memory. The processor may perform voiceidentification by comparing the data 422 related to User 2's voice,which is received in each frequency band and stored in the memory, withsound data which is thereafter received in each frequency band. Forexample, the processor may compare the data 422 related to User 2'svoice, which is received in the first band 310, the second band 320, andthe third band 330 and stored in the memory, with sound data which isthereafter received in the first band 310, the second band 320, and thethird band 330. The processor may determine, using a voiceidentification algorithm based on results of the comparison, whether theuser who has input the current sound data is User 2 (for example, voiceidentification success or voice identification failure).

Referring to FIG. 4, the energy of sound data sensed by the sound sensormay have a distribution in the third band 330, which corresponds to theintersection between the “third band 330” column and a “User n's voice”row (data 421, 422, or 423 related to User n's voice). For example,voice identification may be performed to identify whether a user whoinputs sound data into the electronic device is User 3. Beforeperforming voice identification for identifying User 3, the processormay store the data 423 related to User 3's voice in the memory. In thiscase, the processor may receive the data 423 related to User 3's voicein each frequency band through the sound sensor configured to sensesound data in each frequency band, and may store the data 423 related toUser 3's voice in the memory. The processor may perform voiceidentification by comparing the data 423 related to User 3's voice,which is received in each frequency band and stored in the memory, withsound data which is thereafter received in each frequency band. Forexample, the processor may compare the data 423 related to User 3'svoice, which is received in the first band 310, the second band 320, andthe third band 330 and stored in the memory, with sound data which isthereafter received in the first band 310, the second band 320, and thethird band 330. The processor may determine, using a voiceidentification algorithm based on results of the comparison, whether theuser who has input the current sound data is User 3 (for example, voiceidentification success or voice identification failure).

Referring to FIG. 4, the processor may receive sound data which is inputto the electronic device and may determine, through voiceidentification, whether the sound data is that of a registered user ofthe electronic device (for example, User 1, User 2, or User 3). Forexample, the processor may perform voice identification by comparing, ineach frequency band (for example, the first band 310, the second band320, and the third band 330), sound data received through the soundsensor with the data 421 related to User 1's voice, the data 422 relatedto User 2′ voice, and the data 423 related to User 3's voice to finddata matching the received sound data. The processor may compare sounddata received in each frequency band with the energy of the data 421related to User 1's voice, the energy of the data 422 related to User 2′voice, and the energy of the data 423 related to User 3's voice whichare stored according to frequency bands in the form of a table. Forexample, when arbitrary sound data is input, the processor may receivetable-type data from the memory to determine whether the sound data isthat of a user registered in the electronic device. The processor maycompare input arbitrary sound data with the received table-type data ineach frequency band (for example, the first band 310, the second band320, and the third band 330). When results of the comparison in eachfrequency band shows that the input arbitrary sound data matches thedata 421 related to User 1's voice in two bands or more, the processormay determine that the voice identification is successful. When resultsof the comparison in each frequency band shows that the input arbitrarysound data matches the data 421 related to User 1's voice only in oneband, the processor may determine that the voice identification isunsuccessful. Also, when results of the comparison in each frequencyband shows that the input arbitrary sound data does not match the data421 related to User 1's voice in any band, the processor may determinethat the voice identification is unsuccessful. For example, when theprocessor determines that the input arbitrary sound data matches thedata 421, 422, or 423 related to User n's voice in more than fiftypercent of n frequency bands (for example, n refers to an add number),the processor may determine that the voice identification is successfulfor User n. When the processor determines that the input arbitrary sounddata matches the data 421, 422, or 423 related to User n's voice in lessthan fifty percent of the n frequency bands (for example, n refers to anadd number), the processor may determine that the voice identificationis unsuccessful for User n.

FIG. 5 is an example view illustrating a voice identification method fordetermining whether there is an error in voice data of a user accordingto various example embodiments.

Referring to FIG. 5, a decision on voice identification may be denotedas successful or unsuccessful. For example, a decision of 1 may refer toa successful voice identification. For example, a decision of 0 mayrefer to an unsuccessful voice identification. A successful voiceidentification may refer to the case in which decision isconsistently 1. For example, the case in which decision isinconsistently 1 or 0 may refer to an unsuccessful voice identification.The case in which decision is inconsistently 1 or 0 may be determined asa case in which an error occurs during voice identification. Asuccessful voice identification may refer to the case in which inputsound data matches data related to the voice of at least one user amonga plurality of registered users of an electronic device. An unsuccessfulvoice identification may refer to the case in which input sound datadoes not match data related to any one of the plurality of registeredusers of the electronic device.

Referring to FIG. 5, decisions on voice identification, which are shownon the left and right sides, may be obtained based on different piecesof sound data, respectively. For example, nine decisions on voiceidentification which are shown on the left side may be obtained based onthe same sound data. For example, nine decisions on voice identificationwhich are shown on the right side may be obtained based on sound datadifferent from the sound data which is used to derive the nine decisionson the left side. A decision 530 on the lowest side in FIG. 5 may be adecision derived by performing voice identification in seven bands 520and then applying a preset determination algorithm (for example, avoting algorithm, a weighted sum algorithm, or the like) to resultsthereof.

FIG. 5 shows decisions on voice identification which is performed onsound data input to an electronic device (for example, the electronicdevice 100 shown in FIG. 1). Referring to FIG. 5, in an entire frequencyband 510, decisions shown on the left and right sides may be differentfrom each other. For example, the left decision in the entire frequencyband 510 may indicate a successful voice identification (for example,results of decision are always 1) in which data is recognized as voicedata of one of a plurality of registered users of the electronic device.For example, the decision in the entire frequency band 510 which isshown on the right side have voice identification errors because data isinconsistently recognized as being or not being voice data of one of theplurality of registered users of the electronic device, that is, a voiceidentification failure (for example, results of decision areinconsistently 1 and 0)

Referring to FIG. 5, a processor (for example, the processor 110 shownin FIG. 1) may receive sound data in the seven bands 520 through a soundsensor (for example, the sound sensor 120 shown in FIG. 1) and mayperform voice identification on the sound data. For example, theprocessor may receive sound data in each of the seven bands 520, thatis, in a first band 521, a second band 522, a third band 523, a fourthband 524, a fifth band 525, a sixth band 526, and a seventh band 527,and may determine whether the sound data in each of the seven bands 520is that of a user of the plurality of registered users of the electronicdevice. The sound data, which the processor has received in the sevenbands 520, that is, the first to seventh bands 521 to 527, may becompared, in each of the seven bands 520, with voice data of theplurality of registered users which is stored in a memory (for example,the memory 130).

Referring to FIG. 5, among the left decisions on voice identification inthe seven bands 520, the decisions in the first band 521, the secondband 522, the third band 523, the fourth band 524, the fifth band 525,and the seventh band 527 are consistently 1. However, the decision inthe sixth band 526 is 0 twice with time, that is, there are errorsindicating that the decision is not consistently 1. Among the leftdecisions on voice identification in the seven bands 520, there areerrors only in one band (for example, the sixth band 526), and thedecisions in bands more than a preset number of bands (for example,three bands) are consistently 1, such that the decision 530 finally madeon voice identification may be a success.

Referring to FIG. 5, among the right decisions on voice identificationin the seven bands 520, the decisions in the sixth band 526 and theseventh band 527 are consistently 0. However, the decisions in the firstband 521, the second band 522, the third band 523, the fourth band 524,and the fifth band 525 are inconsistently 0 and 1 with time, showingerrors. Among the decisions on voice identification in the seven bands520 which are shown on the right side, errors are present in five bands(for example, the first band 521, the second band 522, the third band523, the fourth band 524, and the fifth band 525), but the decisions are0 in bands more than a preset number of bands (for example, three bands)such that the decision 530 finally made on voice identification may be afailure.

FIG. 6 is a flowchart illustrating a voice identification methodaccording to various example embodiments.

Referring to FIG. 6, in operation 610, a processor (for example, theprocessor 110 shown in FIG. 1) of an electronic device (for example, theelectronic device 100 shown in FIG. 1) may receive sound data from asound sensor (for example, the sound sensor 120 shown in FIG. 1). Thesound data may include a first piece of data corresponding to a firstfrequency band and a second piece of data corresponding to a secondfrequency band. For example, the sound sensor may include a plurality ofmechanical oscillators capable of sensing sound data in differentfrequency bands, and may thus sense sound data in different frequencybands by using the plurality of mechanical oscillators. That is, thesound sensor may sense sound data distinguishably in the first frequencyband and the second frequency band by using at least one mechanicaloscillator capable of sensing sound data in the first frequency band andat least one mechanical oscillator capable of sensing sound data in thesecond frequency band. The processor may receive the sound data which issensed in the frequency bands by the sound sensor.

Referring to FIG. 6, in operation 620, the processor may receive datarelated to the voice of a user from a memory (for example, the memory130 shown in FIG. 1). For example, the user may refer to at least oneuser of a plurality of registered users of the electronic device. Thedata related to user's voice may refer to data related to the voice of aplurality of users registered as users of the electronic device. Thedata related to user's voice may be previously stored in the memory, andwhen a user is additionally registered, the data related to user's voicemay be updated. Here, data may be transmitted, received, and stored byany method without limitations.

Referring to FIG. 6, in operation 630, the processor may perform voiceidentification by comparing the first piece of data and the second pieceof data with the data related to user's voice in each relevant frequencyband. For example, the processor may perform voice identification bycomparing the data related to user's voice with the first piece of datain the first frequency band. In addition, the processor may performvoice identification by comparing the data related to user's voice withthe second piece of data in the second frequency band. In this case, thedata related to user's voice in the first frequency band and the datarelated to user's voice in the second frequency band may refer to voicedata of a registered user which is previously stored in the memory (forexample, the memory 130 shown in FIG. 1).

FIG. 7 is a flowchart illustrating a method of identifying a voice ineach frequency band according to various example embodiments.

Referring to FIG. 7, in operation 710, a processor (for example, theprocessor 110 shown in FIG. 1) of an electronic device (for example, theelectronic device 100 shown in FIG. 1) may receive a first piece of dataand a second piece of data in frequency bands from is a sound sensor(for example, the sound sensor 120 shown in FIG. 1). In an embodiment,the sound sensor may have a plurality of sub-bands (or channels)respectively having characteristics of the frequency bands. A soundinput to the sound sensor may be sensed in each of the frequency bandsthrough the plurality of sub-bands.

The processor may receive frequency-band-based data which is sensed inthe plurality of sub-bands by the sound sensor. For example, theprocessor may receive, from the sound sensor, the first piece of datasensed in a first sub-band (or a first channel) having characteristicsof the first frequency band. In addition, the processor may receive,from the sound sensor, the second piece of data sensed in a secondsub-band (or a second channel) having characteristics of the secondfrequency band different from the first frequency band.

Referring to FIG. 7, in operation 720, the processor may receive datarelated to user's voice in each frequency band from a memory (forexample, the memory 130 shown in FIG. 1). For example, the data relatedto user's voice in each frequency band may refer to data which isrelated to the voice of a registered user of the electronic device andis divided according to frequency bands.

Referring to FIG. 7, in operation 730, the processor may performcomparison and determination (sub-band decision) on the received data ineach frequency band by a trained identification algorithm.

The processor may compare the first piece of data sensed in the firstsub-band having characteristics of the first frequency band with data inthe first frequency band among the data related to the voice of theregistered user. In addition, the processor may compare the second pieceof data sensed in the second sub-band having characteristics of thesecond frequency band with data in the second frequency band among datarelated to the voice of the registered user. The processor may performcomparison in parallel on the data in the first frequency band and thesecond frequency band.

The processor may compare the data received in each frequency band withthe data related to the voice of the registered user which is previouslystored according to the frequency bands, and may accept the receiveddata when the similarity between the data is greater than or equal to adegree of similarity and may reject the received data when thesimilarity between the data is less than the degree of similarity.According to an example embodiment, the degree of similarity may bepreset. According to an example embodiment, the processor may performdetermination in parallel on the first piece of data and the secondpiece of data.

Referring to FIG. 7, in operation 740, the processor may perform votingand final voice identification based on results of the determination onthe data received in each frequency band. For example, when the numberof accepted pieces of data among the received data is greater than thenumber of rejected pieces of data among the received data, the processormay determine results of the voting as acceptance and may thus finallydetermine that voice identification is successful. In another example,when the number of accepted pieces of data among the received data isgreater than a preset number of acceptances, the processor may determineresults of the voting as acceptance and may finally determine that voiceidentification is successful.

FIG. 8 is a flowchart illustrating a process of deriving results in avoice identification method according to various example embodiments.

Referring to FIG. 8, in operation 810, a processor (for example, theprocessor 110 shown in FIG. 1) of an electronic device (for example, theelectronic device 100 shown in FIG. 1) may receive sound data from asound sensor (for example, the sound sensor 120 shown in FIG. 1). Thesound data may include a first piece of data corresponding to a firstfrequency band and a second piece of data corresponding to a secondfrequency band. For example, the sound sensor may include a plurality ofmechanical oscillators capable of sensing sound data in differentfrequency bands, and may thus sense sound data in different frequencybands by using the plurality of mechanical oscillators. That is, thesound sensor may sense sound data distinguishably in the first frequencyband and the second frequency band by using at least one mechanicaloscillator capable of sensing sound data in the first frequency band andat least one mechanical oscillator capable of sensing sound data in thesecond frequency band. The processor may receive the sound data which issensed in the frequency bands by the sound sensor.

Referring to FIG. 8, in operation 820, the processor may compare resultsof voice identification with a threshold value. According to an exampleembodiment, the threshold value may be preset. The processor may comparethe sound data received according to the frequency bands with band-baseddata of data related to user's voice stored in a memory (for example,the memory 130 shown in FIG. 1). For example, the processor maycalculate a first representative value corresponding to the first pieceof data and a second representative value corresponding to the secondpiece of data. The processor may compare a weighted sum of the firstrepresentative value and the second representative value with a presetthreshold value. In another example embodiment, the processor maycalculate a weighted sum of raw data of the first piece of data and rawdata of the second piece of data. The processor may compare thecalculated weighted sum with a preset threshold value.

Referring to FIG. 8, in operation 830, when it is determined thatresults of the calculation is greater than a preset threshold valueduring voice identification, the processor may determine that the voiceidentification is successful. For example, when more than fifty percentof results of determination in the first frequency band, determinationin the second frequency band, . . . , and determination in an n-thfrequency band (for example, n is an odd number) are acceptance results(for example, if a value 1, among 1 and 0, refers to acceptance, and thenumber of 1s is greater than n/2), the processor may determine that thevoice identification is successful. In operation 840, when it isdetermined that results of the calculation are less than the presetthreshold value during voice identification, the processor may determinethat the voice identification is unsuccessful. For example, when morethan fifty percent of results of determination in the first frequencyband, determination in the second frequency band, . . . , anddetermination in the n-th frequency band (for example, n is an oddnumber) are rejection results (for example, if a value, 1 among 1 and 0refers to acceptance, and the number of 0s is greater than n/2), theprocessor may determine that the voice identification is unsuccessful.After determining that the voice identification is unsuccessful inoperation 840, the processor may return to operation 810 and performvoice identification on sound data.

FIGS. 9A to 9C are example views illustrating voice identificationmethods according to various example embodiments.

Referring to FIG. 9A, a processor (for example, the processor 110 shownin FIG. 1) may perform band-based calculation on at least one sound datasensed through a sound sensor (for example, the sound sensor 120 shownin FIG. 1). The processor may be included in a voice identificationmodule 910. For example, the processor may calculate a firstrepresentative value corresponding to a first piece of data and a secondrepresentative value corresponding to a second piece of data. Theprocessor may compare a weighted sum of the first representative valueand the second representative value with a preset threshold value. Eachof the representative values may be a value obtained by multiplying rawdata by a preset constant, and may be indicated as a decision of 1 or 0.For example, a decision of 1 may refer to a successful voiceidentification, and a decision of 0 may refer to an unsuccessful voiceidentification. For example, the processor may compare representativevalues in first to nth bands (for example, n is an odd number) withpreviously stored data related to user's voice in each band to derivedecisions (sub-band decisions) and then may derive a final decisionthrough a final determination algorithm. In addition, the processor maydetermine whether voice identification is successful by combining thederived decisions.

Referring to FIG. 9B, the processor may calculate a weighted sum of rawdata (for example, 0.889, 0.93, and 0.6) of the first piece of data andthe second piece of data. The processor may be included in a voiceidentification module 920. The processor may compare the calculatedweighted sum with a preset threshold value. While performing voiceidentification, the processor may determine that the voiceidentification is successful when results of the calculation are greaterthan a preset threshold value. While performing voice identification,the processor may determine that the voice identification isunsuccessful when results of the calculation are less than the presetthreshold value.

Referring to FIG. 9C, based on results of voting regarding the number ofaccepts and the number of rejects, the processor may determine voiceidentification as successful or unsuccessful. The processor may beincluded in a voice identification module 930. When more than fifthpercent of results of voting in a first band, results of voting in asecond band, . . . , and results of voting in an nth band (for example,n refers to an odd number) are accepts (for example, there are acceptsand rejects, and the number of accepts is greater than n/2), theprocessor may determine that the voice identification is successful.When more than fifth percent of results of decision in the first band,results of decision in the second band, . . . , and results of decisionthe nth band (for example, n refers to an odd number) are rejects (forexample, there are accepts and rejects, and the number of rejects isgreater than n/2), the processor may determine that the voiceidentification is unsuccessful.

FIGS. 10A and 10B are example views illustrating voice identificationmethods using neural networks according to various example embodiments.

Referring to FIG. 10A, a processor (for example, the processor 110 shownin FIG. 1) may perform band-based calculation on at least one sound datasensed through a sound sensor (for example, the sound sensor 120 shownin FIG. 1). The processor may be included in a voice identificationmodule 1010. For example, the processor may calculate a firstrepresentative value corresponding to a first piece of data and a secondrepresentative value corresponding to a second piece of data. Theprocessor may compare a weighted sum of the first representative valueand the second representative value with a preset threshold value. Eachof the representative values may be a value obtained by multiplying rawdata by a preset constant, and may be indicated as a decision of 1 or 0.For example, a decision of 1 may refer to a successful voiceidentification, and a decision of 0 may refer to an unsuccessful voiceidentification. For example, the processor may compare representativevalues in first to nth bands (for example, n is an odd number) withpreviously stored data related to user's voice in each band to derivedecisions (sub-band decisions) and then may determine whether voiceidentification is successful by combining the derived decisions. Thevoice identification module 1010 may determine, using a neural network,whether voice identification is successful. For example, the neuralnetwork may repeatedly compare data related to the voice of a pluralityof registered users of an electronic device (for example, the electronicdevice 100 shown in FIG. 1) with the received sound data. The neuralnetwork may use results obtained by repeating voice identification toadaptively perform voice identification by considering sound datavariations caused by variations in the body conditions of the users. Theneural network may include a processor or may additionally include aneural processor.

Referring to FIG. 10B, the processor may calculate a weighted sum of rawdata (for example, 0.889, 0.93, and 0.6) of the first piece of data andthe second piece of data. The processor may be included in a voiceidentification module 1020. The processor may compare the calculatedweighted sum with a preset threshold value. While performing voiceidentification, the processor may determine that the voiceidentification is successful when results of the calculation are greaterthan a preset threshold value. While performing voice identification,the processor may determine that the voice identification isunsuccessful when results of the calculation are less than the presetthreshold value. For example, the neural network may repeatedly comparedata related to the voice of a plurality of registered users of anelectronic device with the received sound data. The neural network mayuse results obtained by repeating voice identification to adaptivelyperform voice identification by considering sound data variations causedby variations in the body conditions of the users. The neural networkmay include a processor or may additionally include a neural processor.

Each of the voice identification modules 910, 920, 930, 1010, and 1020shown in FIGS. 9A to 10B may have an algorithm for determining whethervoice identification is successful. For example, each of the voiceidentification modules 910, 920, 930, 1010, and 1020 may have at leastone of the algorithms shown in FIGS. 9A to 10B and may perform voiceidentification using the at least one algorithm.

According to an aspect of an embodiment, an electronic device mayinclude: a memory; a sound sensor; and a processor, wherein theprocessor may be configured to: receive, from the sound sensor, sounddata including a first piece of data corresponding to a first frequencyband and a second piece of data corresponding to a second frequency banddifferent from the first frequency band; receive data related toregistered user's voice from the memory; perform voice identification bycomparing the first piece of data and the second piece of data with thedata related to the registered user's voice; and determine an outputaccording to results of the voice identification.

The sound sensor may include a plurality of mechanical oscillators, andthe plurality of mechanical oscillators may be configured to sense sounddata according to frequency bands.

The plurality of mechanical oscillators may include at least onemechanical oscillator configured to sense sound data in a first band andat least one mechanical oscillator configured to sense sound data in asecond band.

The data related to the registered user's voice may include user's voicedata in the first frequency band and user's voice data in the secondfrequency band.

When the processor performs the voice identification, the processor maybe further configured to compare the first piece of data with the user'svoice data in the first frequency band, and the second piece of datawith the user's voice data in the second frequency band.

When performing the voice identification, the processor may be furtherconfigured to: calculate a first representative value that is arepresentative value of the first piece of data and a secondrepresentative value that is a representative value of the second pieceof data; and compare a weighted sum of the first representative valueand the second representative value with a preset threshold value.

When performing the voice identification, the processor may be furtherconfigured to: calculate a weighted sum of raw data of the first pieceof data and raw data of the second piece of data; and compare thecalculated weighted sum with a preset threshold value.

The processor may be further configured to: determine, based on resultsof the comparison between the first piece of data and the user's voicedata in the first frequency band, whether the sound data matches theregistered user's voice in the first frequency band; and determine,based on results of the comparison between the second piece of data andthe user's voice data in the second frequency band, whether the sounddata matches the registered user's voice in the second frequency band.

The processor may be further configured to determine that the voiceidentification is successful when the weighted sum is greater than thepreset threshold value.

The processor may be further configured to determine that the voiceidentification is successful when a sum of a result of the determinationin the first frequency band and a result of the determination in thesecond frequency band is greater than a preset threshold value.

According to an aspect of another embodiment, there may be provided amethod of identifying a voice using an electronic device, the methodincluding: receiving, from a sound sensor, sound data including a firstpiece of data corresponding to a first frequency band and a second pieceof data corresponding to a second frequency band different from thefirst frequency band; receiving data related to registered user's voicefrom a memory; performing voice identification by comparing the firstpiece of data and the second piece of data with the data related to theregistered user's voice; and determining an output according to resultsof the voice identification.

The sound sensor may include a plurality of mechanical oscillators, andthe method further may include sensing sound data according to frequencybands by using the plurality of mechanical oscillators.

The sensing of the sound data according to the frequency bands mayinclude: sensing sound data in a first band by using at least one of theplurality of mechanical oscillators; and sensing sound data in a secondband by using at least one of the plurality of mechanical oscillators.

The data related to the registered user's voice may include user's voicedata in the first frequency band and user's voice data in the secondfrequency band.

The performing of the voice identification may include: comparing thefirst piece of data with the user's voice data in the first frequencyband; and comparing the second piece of data with the user's voice datain the second frequency band.

The performing of the voice identification further may include:calculating a first representative value that is a representative valueof the first piece of data and a second representative value that is arepresentative value of the second piece of data; and comparing aweighted sum of the first representative value and the secondrepresentative value with a preset threshold value.

The performing of the voice identification further may include:calculating a weighted sum of raw data of the first piece of data andraw data of the second piece of data; and comparing the calculatedweighted sum with a preset threshold value.

The method may further include: determining, based on results of thecomparing between the first piece of data and the user's voice data inthe first frequency band, whether the sound data matches the registereduser's voice in the first frequency band; and determining, based onresults of the comparing between the second piece of data and the user'svoice data in the second frequency band, whether the sound data matchesthe registered user's voice in the second frequency band.

The determining of the output may include determining that the voiceidentification is successful when the weighted sum is greater than thepreset threshold value.

The determining of the output may include determining that the voiceidentification is successful when a sum of a result of the determiningin the first frequency band and a result of the determining in thesecond frequency band is greater than a preset threshold value.

As described above, according to the one or more of the above exampleembodiments, in the voice identification method, user's voice data maybe received through a multi-band sound sensor capable of sensing voicedata distinguishably according to frequency bands. When the sound sensorof the electronic device receives sound data distinguishably accordingto frequency bands, user's voice identification may be performed in eachfrequency band. When the sound sensor includes a plurality of mechanicaloscillators, the electronic device may increase the accuracy of voiceidentification by performing the voice identification according tocharacteristics of a plurality of frequency bands.

When performing user's voice identification, the electronic device mayreceive voice data in each frequency band through the sound sensor. Theelectronic device may reduce errors in voice identification bycomparing, in each frequency band, the received voice data with voicedata of users updated and stored in a memory.

It should be understood that example embodiments described herein shouldbe considered in a descriptive sense only and not for purposes oflimitation. Descriptions of features or aspects within each exampleembodiment should typically be considered as available for other similarfeatures or aspects in other example embodiments. While one or moreexample embodiments have been described with reference to the figures,it will be understood by those of ordinary skill in the art that variouschanges in form and details may be made therein without departing fromthe spirit and scope as defined by the following claims.

What is claimed is:
 1. An electronic device comprising: a memory; asound sensor; and a processor configured to: receive, from the soundsensor, sound data comprising a first piece of data corresponding to afirst frequency band and a second piece of data corresponding to asecond frequency band different from the first frequency band; receivevoice data related to a voice of a registered user from the memory;perform voice identification by comparing the first piece of data andthe second piece of data with the voice data related to the voice of theregistered user; and determine an output based on a result of the voiceidentification.
 2. The electronic device of claim 1, wherein the soundsensor comprises a plurality of mechanical oscillators configured tosense the sound data according to frequency bands.
 3. The electronicdevice of claim 2, wherein the plurality of mechanical oscillatorscomprise: at least one first mechanical oscillator configured to sensethe sound data in a first band; and at least one second mechanicaloscillator configured to sense the sound data in a second band.
 4. Theelectronic device of claim 1, wherein the voice data related to thevoice of the registered user comprises first voice data in the firstfrequency band and second voice data in the second frequency band. 5.The electronic device of claim 4, the processor is further configured toperform the voice identification by comparing the first piece of datawith the first voice data in the first frequency band, and the secondpiece of data with the second voice data in the second frequency band.6. The electronic device of claim 5, wherein the processor is furtherconfigured to perform the voice identification by: determining a firstrepresentative value corresponding to the first piece of data and asecond representative value corresponding to the second piece of data;and comparing a weighted sum of the first representative value and thesecond representative value with a threshold value.
 7. The electronicdevice of claim 5, wherein the processor is further configured toperform the voice identification by: determining a weighted sum of rawdata of the first piece of data and raw data of the second piece ofdata; and comparing the weighted sum with a threshold value.
 8. Theelectronic device of claim 5, wherein the processor is furtherconfigured to: determine, based on a result of the comparing the firstpiece of data and the first voice data in the first frequency band,whether the sound data matches the voice of the registered user in thefirst frequency band; and determine, based on a result of the comparingthe second piece of data and the second voice data in the secondfrequency band, whether the sound data matches the voice of theregistered user in the second frequency band.
 9. The electronic deviceof claim 6, wherein the processor is further configured to determinethat the voice identification is successful when the weighted sum isgreater than the threshold value.
 10. The electronic device of claim 8,wherein the processor is further configured to determine that the voiceidentification is successful when a sum of a result of the determinationin the first frequency band and a result of the determination in thesecond frequency band is greater than a threshold value.
 11. A method ofidentifying a voice using an electronic device, the method comprising:receiving, from a sound sensor, sound data comprising a first piece ofdata corresponding to a first frequency band and a second piece of datacorresponding to a second frequency band different from the firstfrequency band; receiving voice data related to a voice of a registereduser from a memory; performing voice identification by comparing thefirst piece of data and the second piece of data with the voice datarelated to the voice of the registered user; and determining an outputbased on a result of the voice identification.
 12. The method of claim11, wherein the sound sensor comprises a plurality of mechanicaloscillators, and the method further comprises sensing the sound dataaccording to frequency bands by using the plurality of mechanicaloscillators.
 13. The method of claim 12, wherein the sensing of thesound data according to the frequency bands comprises: sensing the sounddata in a first band by using at least one first mechanical oscillatoramong the plurality of mechanical oscillators; and sensing sound data ina second band by using at least one second mechanical oscillator amongthe plurality of mechanical oscillators.
 14. The method of claim 11,wherein the voice data related to the voice of the registered usercomprises first voice data in the first frequency band and second voicedata in the second frequency band.
 15. The method of claim 14, whereinthe performing of the voice identification comprises: comparing thefirst piece of data with the first voice data in the first frequencyband; and comparing the second piece of data with the second voice datain the second frequency band.
 16. The method of claim 15, wherein theperforming of the voice identification further comprises: determining afirst representative value corresponding to the first piece of data anda second representative value corresponding to the second piece of data;and comparing a weighted sum of the first representative value and thesecond representative value with a threshold value.
 17. The method ofclaim 15, wherein the performing of the voice identification furthercomprises: determining a weighted sum of raw data of the first piece ofdata and raw data of the second piece of data; and comparing theweighted sum with a threshold value.
 18. The method of claim 15, furthercomprising: determining, based on result of the comparing the firstpiece of data and the first voice data in the first frequency band,whether the sound data matches the voice of the registered user in thefirst frequency band; and determining, based on a result of thecomparing the second piece of data and the second voice data in thesecond frequency band, whether the sound data matches the voice of theregistered user in the second frequency band.
 19. The method of claim16, wherein the determining of the output comprises determining that thevoice identification is successful when the weighted sum is greater thanthe threshold value.
 20. The method of claim 18, wherein the determiningof the output comprises determining that the voice identification issuccessful when a sum of a result of the determining in the firstfrequency band and a result of the determining in the second frequencyband is greater than a threshold value.